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+<table><tr><td>Property group</td><td>Property Name</td><td>Description</td><td>Misc.</td><td>Note</td></tr><tr><td>Model File</td><td>Save Model File</td><td>Decide whether to save model file.</td><td>Required</td><td>Yes, No</td></tr><tr><td>Model File</td><td>Model File Path</td><td>Set the path for the model file to be saved.</td><td>Conditionally Required</td><td/></tr><tr><td>Selection options</td><td>Number of Clusters</td><td>Enter the number of clusters.</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Max Iteration</td><td>Maximum number of iterations. Default is 100</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Initial Runs</td><td>Number of times the algorithm will run with different centroid seeds</td><td>Required</td><td>Integer</td></tr><tr><td>Selection options</td><td>Distance Measure</td><td>Select the distance metric. Default is Euclidean.</td><td>Required</td><td>Euclidean, Squared Euclidean, Manhattan, Minkowski</td></tr><tr><td>Selection options</td><td>Minkowski Coefficient</td><td>Set the coefficients when distance measurement is Minkowski distance. If the coefficient is 1, it is same as the Manhattan distance. If it is 2, it is same as Euclidean distance.</td><td>Conditionally Required</td><td/></tr><tr><td>Selection options</td><td>Preprocessing Method</td><td>Standardization, Mean Correction</td><td>Required</td><td>None, Standardization, Mean Correction</td></tr></table>